SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 976–1000 of 7282 papers

TitleStatusHype
Bright-NeRF:Brightening Neural Radiance Field with Color Restoration from Low-light Raw Images—0
Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations—0
Consistent Human Image and Video Generation with Spatially Conditioned DiffusionCode0
DiffSim: Taming Diffusion Models for Evaluating Visual SimilarityCode1
DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On—0
Guided Diffusion Model for Sensor Data Obfuscation—0
Qua^2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models—0
Enhancing Diffusion Models for High-Quality Image Generation—0
DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation—0
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy ResponseCode1
Personalized Generative Low-light Image Denoising and Enhancement—0
Federated Unlearning Model Recovery in Data with Skewed Label Distributions—0
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning—0
MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing—0
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion—0
VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image EnhancementCode0
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling—0
C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory PredictionCode0
Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K SamplingCode0
Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling—0
LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers—0
Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image GenerationCode0
Learning of Patch-Based Smooth-Plus-Sparse Models for Image ReconstructionCode0
Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask LearningCode2
Unsupervised Region-Based Image Editing of Denoising Diffusion Models—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified